AI Governance Policy Template: Scope, Decisions, Controls, and Evidence

Begin with the policy's management purpose: establishing visibility, accountable use, risk-based approval, control operation, evidence, and intervention. Scope should cover internally developed AI, purchased services, embedded vendor features, models and APIs, employee tools, pilots, and automated agents when they support organizational work. Define candidate and uncertainty handling so teams disclose questionable use rather than interpreting silence as exclusion.

Permitted-use language should identify conditions, not merely approved product names. A licensed assistant may be permitted for public-information drafting but restricted for personal data, privileged material, consequential recommendations, production code, or autonomous action. Restricted activity should identify the decision required, responsible authority, minimum safeguards, and evidence. Prohibited activity should be narrow, explicit, and connected to an authorized containment route.

A policy clause should express the required outcome, applicability, accountable role, trigger, and minimum evidence. “AI must be used responsibly” offers no test. A stronger clause requires every material AI use to have an inventory identifier, approved purpose, accountable business owner, current risk decision, applicable controls, and review date before production use. Detailed field definitions can remain in the inventory standard.

Policy approval should involve the executive sponsor and the functions that must operate or challenge it. Legal, privacy, security, risk, procurement, data, HR, and technology input should be proportionate to scope, but consultation must not dissolve accountability. Record material disagreement, unresolved dependencies, implementation dates, superseded versions, and the authority accepting transitional exposure.

A practical document hierarchy separates enduring management intent from implementation detail. The policy defines scope, authority, boundaries, and required outcomes. Standards translate those outcomes into mandatory criteria for inventory, assessment, procurement, development, access, data, evaluation, human oversight, monitoring, incidents, evidence, and retirement. Procedures describe the operating steps and systems; guidance offers non-mandatory examples. This separation allows control owners to update technical implementation without reopening executive policy approval for every operational refinement.

Control the relationships between documents. Each standard should cite the policy authority, owner, effective date, approving body, exceptions route, and review trigger. Each policy clause should map to one or more implementation owners and evidence sources. If two documents conflict, the hierarchy and escalation route must be explicit. Local business or jurisdictional supplements may strengthen the enterprise baseline, but should not silently weaken it or redefine central decision rights.

Training and communication should be role-based. General users need recognizable permitted, restricted, and prohibited scenarios plus a disclosure route. Product, procurement, data, security, and control owners need the requirements relevant to their decisions. Executives need appetite, approval, exception, and intervention responsibilities. Record acknowledgement only where it supports a real accountability objective; completion statistics alone do not demonstrate that teams can apply the policy.

Set a periodic review date and event triggers. Triggered review may follow a material incident, recurring exception, significant regulatory or contractual change, acquisition, new autonomous capability, major supplier change, control failure, or evidence that employees cannot interpret a rule. The owner should assess whether the policy, an underlying standard, implementation capacity, or communication must change and retain the rationale when no amendment is required.

Before approval, test representative scenarios with business and control owners. Ask whether a team can classify the use, identify the decision-maker, locate the procedure, produce the expected record, and escalate uncertainty. A policy that assumes unavailable tooling, undefined owners, or review capacity should record a phased implementation and interim safeguards rather than declaring immediate compliance.

Policy maintenance should use evidence from exceptions, incidents, control failures, employee questions, supplier changes, and review findings. Frequent exceptions may reveal an impractical rule, missing approved capability, or weak decision route. Update the policy when the boundary changes; update standards and procedures when implementation changes without altering the governing intent.

The wider baseline can be tested through an AI governance assessment, while implementation requirements belong in the AI governance controls architecture.

For employee use outside approved processes, apply the specialist Shadow AI policy framework rather than overloading the enterprise policy with detection procedures.

Decision forums and handoffs should align with the AI governance operating model.

Accountability clauses should remain consistent with documented AI governance roles and responsibilities.